This is a pre-production deployment of Warehouse, however changes made here WILL affect the production instance of PyPI.
Latest Version Dependencies status unknown Test status unknown Test coverage unknown
Project Description

# sparsegrad - automatic computation of sparse Jacobian matrices from `numpy` expressions

`sparsegrad` performs automatic differentiation of vector valued functions in Python. A significant subset of `numpy` operation is supported on Python scalars and `ndarrays` with dimensionality less than 2:

  • all arithmetic operators
  • all elementary functions
  • simple and fancy indexing
  • matrix-vector product `dot`, restricted to constant matrix
  • concatenation of vectors `stack`
  • vectorized selection `where`
  • sum reduction `sum`

Depending on use, `sparsegrad` can provide Jacobian matrix or sparsity pattern.

The primary use of `sparsegrad` is to automatically evaluate Jacobian matrices when solving non-linear systems of equations. `sparsegrad` uses forward mode automatic differentiation. In contrast to backward mode automatic differentiation, this allows to better control the memory usage of calculation.

`sparsegrad` is Python-only and requires only `numpy` and `scipy`. It works both in Python 2.7 and 3.x. In contrast to other pure Python automatic differentiation modules, `sparsegrad` attempts to be better suited for calculating moderately large sparse matrices. It has been used for solving problems with >1M equations and >20M nonzeros without causing bottleneck in terms of running time or memory usage.

For basic usage, see tutorial `doc/tutorial.ipynb`.

`sparsegrad` is not yet tested on many combinations of `numpy` and `scipy` versions. After installing, it is highly recommended to check if all tests pass by running:

` import sparsegrad sparsegrad.test() `

Release History

Release History

0.0.2

This version

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

Show More

Download Files

Download Files

TODO: Brief introduction on what you do with files - including link to relevant help section.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
sparsegrad-0.0.2.tar.gz (24.3 kB) Copy SHA256 Checksum SHA256 Source Nov 22, 2016

Supported By

WebFaction WebFaction Technical Writing Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS HPE HPE Development Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Rackspace Rackspace Cloud Servers DreamHost DreamHost Log Hosting